Complementary Information and Learning Traps
72 Pages Posted: 25 Oct 2017 Last revised: 30 Sep 2019
Date Written: July 30, 2019
Abstract
We develop a model of social learning from complementary information: Short-lived agents sequentially choose from a large set of flexibly correlated information sources for prediction of an unknown state, and information is passed down across periods. Will the community collectively acquire the best kinds of information? Long-run outcomes fall into one of two cases: (1) efficient information aggregation, where the community eventually learns as fast as possible; (2) "learning traps," where the community gets stuck observing suboptimal sources and information aggregation is inefficient. Our main results identify a simple property of the underlying informational complementarities that determines which occurs. In both regimes, we characterize which sources are observed in the long run and how often.
Keywords: Complementary Information, Information Acquisition, Sequential Learning, Speed of Learning, Information Aggregation
JEL Classification: D81, D83, D62, O32
Suggested Citation: Suggested Citation